Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detection have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.

本研究解决了帕金森病（PD）语音检测模型可解释性不足的问题。通过系统评估多种解释性方法，本研究的创新之处在于使用主流的可解释性技术获得特征归因和显著性图，并定量评估其可信度。研究发现，尽管解释结果与分类器一致，但对于领域专家而言，它们常常未能提供有价值的信息，提示需要改进解释方法以满足临床需求。

探讨解释性方法在帕金森病语音检测中的有效性